{"id":"W4415815242","doi":"10.1080/02723638.2025.2577144","title":"Feeding the green gentrification machine: urban agriculture and the barriers to a just ecological transition in Montréal, Québec","year":2025,"lang":"en","type":"article","venue":"Urban Geography","topic":"Urban Agriculture and Sustainability","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Social Sciences and Humanities Research Council of Canada; Canadian Institutes of Health Research; Michael Smith Health Research BC; Public Health Agency; Public Health Agency of Canada","keywords":"Gentrification; Urban agriculture; Agriculture; Urban geography; Urban ecosystem; Urban ecology; Urban density; Transition (genetics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005611869,0.0001975314,0.0002208714,0.00003879436,0.0006568357,0.0001259806,0.0003400436,0.0001454095,0.00005932157],"category_scores_gemma":[0.0001398401,0.00004928762,0.0001862199,0.001393534,0.0002205722,0.00009020769,0.00006345638,0.0002847833,0.000003147145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005727111,"about_ca_system_score_gemma":0.00001748601,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0426961,"about_ca_topic_score_gemma":0.06408775,"domain_scores_codex":[0.9985712,0.0002607084,0.0002771446,0.0003951612,0.0001765993,0.0003192468],"domain_scores_gemma":[0.9992975,0.0003192868,0.00005898039,0.00009990823,0.00008829431,0.0001360374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006402934,0.0003032909,0.8387266,0.00004355087,0.0001327146,0.0000102232,0.01081679,0.00006196665,0.005236622,0.00754939,0.1206938,0.01578474],"study_design_scores_gemma":[0.0003961702,0.00008206505,0.8945348,0.00001224427,0.00005216158,0.000002421935,0.005508723,0.00008355875,0.00003410966,0.0005918674,0.09855065,0.0001512296],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9050531,0.004668746,0.00001096422,0.08834038,0.00006728301,0.001063152,0.00002462946,0.00006118862,0.0007105771],"genre_scores_gemma":[0.9958621,0.0000952176,0.000009136736,0.003260606,0.0001142643,0.0001684889,0.00003110592,7.032522e-7,0.000458342],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09080905,"threshold_uncertainty_score":0.9636787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003946400646374863,"score_gpt":0.1758323648694624,"score_spread":0.1718859642230875,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}